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Predicts and explores important nodes and subnetworks in a given network by several topological algorithms

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cytoHubba

This project is provided by Institute of Information Science, Academia Sinica. TAIWAN

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Team members:

Chia-Hao Chin ( Institute of Information Science, Academia Sinica, Taiwan)
Po-Wen Chen (Department of Computer Science and Imformation Engineering, National Taiwan University, Taiwan)
Chin-Wen Ho ( National Central University, Taiwan)
Shu-Hwa Chen ( Institute of Information Science, Academia Sinica, Taiwan)
Ming-Tat Ko ( Institute of Information Science, Academia Sinica, Taiwan)
Chung-Yen Lin ( Institute of Information Science, Academia Sinica, Taiwan)

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▲Description

Explore important nodes/hubs and fragile motifs in an interactome network by several topological algorithms including Degree, Edge Percolated Component (EPC), Maximum Neighborhood Component (MNC), Density of Maximum Neighborhood Component (DMNC), Maximal Clique Centrality (MCC) and centralities based on shortest paths, such as Bottleneck (BN), EcCentricity, Closeness, Radiality, Betweenness, and Stress.

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Predicts and explores important nodes and subnetworks in a given network by several topological algorithms

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